def rec_sirtfbp(data, theta, rot_center, start=0, test_sirtfbp_iter=False):
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
if test_sirtfbp_iter:
nCol = data.shape[2]
output_name = './test_iter/'
num_iter = [50, 100, 150]
filter_dict = sirtfilter.getfilter(nCol,
theta,
num_iter,
filter_dir='./')
for its in num_iter:
tomopy_filter = sirtfilter.convert_to_tomopy_filter(
filter_dict[its], nCol)
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm='gridrec',
filter_name='custom2d',
filter_par=tomopy_filter)
output_name_2 = output_name + 'sirt_fbp_%iiter_slice_' % its
dxchange.write_tiff_stack(data,
fname=output_name_2,
start=start,
dtype='float32')
# Reconstruct object using sirt-fbp algorithm:
num_iter = 100
nCol = data.shape[2]
sirtfbp_filter = sirtfilter.getfilter(nCol,
theta,
num_iter,
filter_dir='./')
tomopy_filter = sirtfilter.convert_to_tomopy_filter(sirtfbp_filter, nCol)
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm='gridrec',
filter_name='custom2d',
filter_par=tomopy_filter)
return rec
def rec_sirtfbp(data, theta, rot_center, start=0, test_sirtfbp_iter = True):
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
if test_sirtfbp_iter:
nCol = data.shape[2]
output_name = './test_iter/'
num_iter = [50,100,150]
filter_dict = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
for its in num_iter:
tomopy_filter = sirtfilter.convert_to_tomopy_filter(filter_dict[its], nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
output_name_2 = output_name + 'sirt_fbp_%iiter_slice_' % its
dxchange.write_tiff_stack(data, fname=output_name_2, start=start, dtype='float32')
# Reconstruct object using sirt-fbp algorithm:
num_iter = 100
nCol = data.shape[2]
sirtfbp_filter = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
tomopy_filter = sirtfilter.convert_to_tomopy_filter(sirtfbp_filter, nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
return rec
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True)
# Set rotation center.
rot_center = 1552
print(rot_center)
data = tomopy.minus_log(data)
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
test_sirtfbp_iter = True
if test_sirtfbp_iter:
nCol = data.shape[2]
output_name = './test_iter/'
num_iter = [50,100,150]
filter_dict = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
for its in num_iter:
tomopy_filter = sirtfilter.convert_to_tomopy_filter(filter_dict[its], nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
output_name_2 = output_name + 'sirt_fbp_%iiter_slice_' % its
dxchange.write_tiff_stack(data, fname=output_name_2, start=start, dtype='float32')
# Reconstruct object using sirt-fbp algorithm:
num_iter = 100
nCol = data.shape[2]
sirtfbp_filter = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
tomopy_filter = sirtfilter.convert_to_tomopy_filter(sirtfbp_filter, nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
# Reconstruct object using Gridrec algorithm.
# rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', nchunk=1)
